RELEVANCE

The emergence and improvement of next-generation sequencing technology (NGS) has led to a decrease in the cost and time required for carrying out molecular genetic studies of the human genome. The availability and effectiveness of NGS creates the preconditions for the large-scale use of this technology in government programs for mass screening of hereditary-caused diseases.

OBJECTIVE

The present study was aimed at development of neonatal screening algorithm for monogenic hereditary metabolic diseases: cystic fibrosis, phenylketonuria and galactosemia, with the inclusion of NGS technology.

MATERIAL AND METHODS

The study included 196 217 newborns, examined in the framework of the existing neonatal screening algorithm in the period from January 01, 2015 to January 01, 2018. Material for analysis was DNA isolated from dried blood spots. Biochemical methods and the NGS method for detection 442 mutations in the CFTR gene, mutations in PAH gene 99, 40 mutations in a gene of GALT, described in the databases CFTR1, CFTR2, dbSNP, PAHdb were used.

RESULTS

The DNA test performed using high-throughput sequencing (NGS) was successfully conducted in three groups of newborns (n=858) with increased results of neonatal screening for cystic fibrosis, phenylketonuria and galactosemia. Molecular genetic diagnosis was established in 8% (69 out of 858) of those surveyed, of them in the group with suspected cystic fibrosis — 12% (32 out of 264), phenylketonuria — 43.75% (35 out of 80), galactosemia — 0.4% (2 out of 514). All the mutations detected were tested using Sanger’s sequencing method. In the remaining 92% (789 out of 858) surveyed, the results of biochemical neonatal screening were false positives.

CONCLUSION

Analysis of the results of the screening protocol using NGS showed 100% sensitivity, specificity, and positive predictive value. The data obtained demonstrate that the target panel is useful as a fast, high-performance and cost-effective tool for simultaneous analysis of multiple genes in the newborn screening of monogenic diseases. Based on the data obtained, the proposed algorithm was introduced into the neonatal screening program of St.Petersburg.